Mathematical Methods of Artificial Intelligence
Faculty of Computer Science and Cybernetics
            Name
        
        
            Mathematical Methods of Artificial Intelligence
        
    
            Program code
        
        
            122
        
    
            Qualification awarded
        
        
            Master of Computer Science
        
    
            Length of programme
        
        
2 years        
    
            Number of credits
        
        
            120
        
    
            level of qualification according to the National Qualification Framework and the European Qualifications Framework 
        
        
            7
        
    
            Qualification level
        
        
            Second (Master)                                   
        
    
            Discipline
        
        
            Information technologies
        
    
            Speciality
        
        
KnowledgeField EN        
    
            Specific admission requirements
        
        
Bachelor's degree
        
    
            Specific arrangements for recognition of prior learning
        
        
            
On the basis of entrance examinations conducted in the form of:
• a single entrance exam in a foreign language in the form of a test; • professional entrance examination conducted by the University.
        
    • a single entrance exam in a foreign language in the form of a test; • professional entrance examination conducted by the University.
            Qualification requirements and regulations, including graduation requirements
        
        
            Qualification: "Master of Computer Science" (defense of a comprehensive exam in computer science, defense of a master's thesis). Additional qualifications "Junior Researcher (Programming)" (grades: 1. Defense of master's thesis>> 90 b. 2. Disciplines "Methodology and organization of research with the basics of intellectual property (in English)"> = 90 b); "Developer of computer programs" (estimates: 1. DVVS> = 75 b; 2. Practices> = 75 b).
        
    
            Programme learning outcomes
        
        
            PLO1. Identify problem situations, perform their research on the basis of a system approach, make informed choice of methods and models for the formation of effective management decisions, apply models and methods of decision making to enterprise development forecasting and to the object domain of computer science.
PLO2. Use models and decision-making methods based on fuzzy set theory in the case of uncertainty and risks in the process of branch management.
PLO3. Master new data tools by processing weblogs, text mining and machine learning, for business processes forecasting and situational management, sentimental analysis of reviews, development of advisory 
systems for electronic commerce, media, social networks, banking, advertising, etc. 
PLO4. Analyze big data and simulate high-level abstractions in large heterogeneous datasets, design big data warehouses, extract data and knowledge, visualize big data, create and evaluate regressive models generated on the basis of big data.
PLO5. Solve complex problems that require high-performance systems to ensure the scalability of parallel algorithms and programs.
PLO6. To use distributed, high-performance computing technologies to ensure effective choice and use of consolidated resources and services. 
PLO7. Use supercomputing systems to implement the multiprocessor programming paradigm. Develop effective parallel algorithms for complex production tasks. Use cloud platforms and their virtualization.
PLO8. Analyze the peculiarities of modern quantum technologies to solve problems,  confidential communication, quantum cryptography, in particular. Research theoretical and experimental aspects of quantum informatics.
PLO9. Be able to use methods and technologies for data organization and application in artificial intelligence problems. Create decision-making models based on the pattern recognition theory, neural networks and fuzzy logic.
http://csc.knu.ua/uk/filer/canonical/1636055939/1430/
        
    
            Form of study
        
        
Full-time form        
    
            Examination regulations and grading scale
        
        
            Meet the requirements of the "Regulations on the organization of the educational process at the Taras Shevchenko National University of Kyiv." http://nmc.univ.kiev.ua/docs/poloz_org_osv_proc-2018.pdf
        
    
            Оbligatory or optional mobility windows (if applicable)
        
        
            Work placement
        
        
            Educational and scientific program with elements of dual education. Internship on the basis of the employer organization in accordance with the contract.
        
    
            Work-based learning
        
        
            Educational and scientific program with elements of dual education. Execution of research work, master's thesis internship on the basis of the employer. Replacement of the employer organization within the organizations with which the cooperation agreement has been concluded under the SNP is possible with the consent of all parties (applicant, program guarantor, mentors from both employers' organizations). employer.
Specialists-practitioners of leading domestic and foreign IT companies are involved in the development and implementation of the program on the basis of relevant agreements, including Samsung Ukraine / Samsung R&D Institute Ukraine, Global Logic, LUN, Avora Ltd. and others subject to agreements.
        
    
                    Director of the course
                
                 
                
                        Igor 
                        O
                        Zavadskyi
                    
                    
                        Mathematical Informatics 
Faculty of Computer Science and Cybernetics
                Faculty of Computer Science and Cybernetics
            Occupational profiles of graduates
        
        
            Professional activity in positions related to the development of mathematical, information and software information systems in the field of information technology.
        
    
            Access to further studies
        
        
            Obtaining education under the educational program of the third (educational-scientific) level of higher education and obtaining additional qualifications in the adult education system.
        
    Subjects
As part of the curriculum, students study the following disciplines
                        Mathematical Methods of Computer Vision
                    
                    
                        Code: ННД.09.,
                        
                    
                
                        Information security
                    
                    
                        Code: ННД.04,
                        
                    
                
                        Deep Learning
                    
                    
                        Code: ННД.02,
                        
                    
                
                        Cоnvex optimization methods
                    
                    
                        Code: ДВС.3.01.03,
                        
                    
                
                        Data Mining Actual Problem
                    
                    
                        Code: ОК.15,
                        
                    
                
                        Logic and the Automated Deduction
                    
                    
                        Code: ННД.16,